@Article{cmc.2021.014265,
AUTHOR = {Saleh Albahli, Ahmad Algsham, Shamsulhaq Aeraj, Muath Alsaeed, Muath Alrashed, Hafiz Tayyab Rauf, Muhammad Arif, Mazin Abed Mohammed},
TITLE = {COVID-19 Public Sentiment Insights: A Text Mining Approach to the Gulf Countries},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {67},
YEAR = {2021},
NUMBER = {2},
PAGES = {1613--1627},
URL = {http://www.techscience.com/cmc/v67n2/41323},
ISSN = {1546-2226},
ABSTRACT = {Social media has been the primary source of information from mainstream news agencies due to the large number of users posting their feedback. The COVID-19 outbreak did not only bring a virus with it but it also brought fear and uncertainty along with inaccurate and misinformation spread on social media platforms. This phenomenon caused a state of panic among people. Different studies were conducted to stop the spread of fake news to help people cope with the situation. In this paper, a semantic analysis of three levels (negative, neutral, and positive) is used to gauge the feelings of Gulf countries towards the pandemic and the lockdown, on basis of a Twitter dataset of 2 months, using Natural Language Processing (NLP) techniques. It has been observed that there are no mixed emotions during the pandemic as it started with a neutral reaction, then positive sentiments, and lastly, peaks of negative reactions. The results show that the feelings of the Gulf countries towards the pandemic depict approximately a 50.5% neutral, a 31.2% positive, and an 18.3% negative sentiment overall. The study can be useful for government authorities to learn the discrepancies between different populations from diverse areas to overcome the COVID-19 spread accordingly.},
DOI = {10.32604/cmc.2021.014265}
}